{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L2损失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回归系数： [167.79594506] 截距项: 123.94211903204723\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "y_train=np.array([368,340,376,954,331,556])\n",
    "X_train=np.array([1.7,1.5,1.3,5,1.3,2.2])\n",
    "plt.scatter(X_train,y_train,label='train samples')\n",
    "plt.legend(loc='upper left')\n",
    "X_train=X_train.reshape(-1,1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr=LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "print(\"回归系数：\",lr.coef_,\"截距项:\",lr.intercept_)\n",
    "yres=lr.predict(X_train)\n",
    "plt.plot(X_train,yres,'g-')\n",
    "for idx, m in enumerate(X_train):\n",
    "    plt.plot([m, m],[y_train[idx],yres[idx]], 'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Huber损失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import HuberRegressor\n",
    "huber=HuberRegressor()\n",
    "huber.fit(X_train,y_train)\n",
    "y_train_pred_huber=huber.predict(X_train)\n",
    "plt.plot(X_train,y_train_pred_huber,'m-',label=\"Huber\")\n",
    "plt.plot(X_train,y_train_pred_huber,'g.',label=\"train samples\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：根据学生学习时长，预测其成绩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "学习时长为7.2的预测分数为： [67.30939984]\n"
     ]
    },
    {
     "data": {
      "image/png": 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DIN9993BqCkvUgW5mqcDJwNRI02PAIUAGsBK4byePG2VmBWZWUFxcXNMhIiJ1dvTRQZCvWFHetmxZEORt24ZXV5jq0kMfBsxx91UA7r7K3UvdvQz4OzCwpge5+3h3z3T3zPbt2ze8YhFp1s49NwjyDz4ob1u4MAjygw4Kr65EUJdAP4cKwy1m1qHCfacBC2NVlIhIVWPGBEH+fIVB3/ffD4K8R4/w6kokUc1DN7M04L+ACnt1cLeZZQAOLK1yn4hITNx/P/z+95Xbpk+vvpiWRBno7r4RaFulrYksKCkiTdGkSdWXrZ0wAS66KJx6moIknmIvIk3RG28EQysVw/yOO4KhFYX5runSfxFJCP/+Nxx5ZOW2K6+Ehx8Op56mSIEuIqH6/HPo3r1y22mnwYsvhlNPU6ZAF5FQrFhRfX/O/v1Bq4PUnwJdRBrV2rWwzz6V2/bZB9asCaeeZKJAF5FGsWkTpKVVby8rq37pvtSPAl1E4mrbtpoXx0r2pWzDoEAXkbhwrzmwt2ypea1yaTj9fhSRmGvRonqY//BDEPIK8/hRoItIzBx6aDAeXnHfnFWrgtt77hleXc2FAl1EGiwnJwjyxYvL2xYvDoJ8333Dq6u5UaCLSL1deGEQ5G+/Xd42b14Q5F26hFdXc6VAF5E6u/HGIMifeqq8LS8vCPI+fcKqShToIhK1sWODIL/jjvK2F14IgvzYY8OrSwKatigitZo8Gc4+u3LbuHFwqXZBSCjqoYvITuXmBj3yimF+881Bj1xhnnjUQxeRambPhszMym2jRsHf/hZOPRIdBbqI7PDVV9C1a+W2E06AGTPCqUfqRoEuInz7LXToULmtRw9YqK3fmxQFukgztn49tGlTua11a9i4MZx6pGEU6CLN0ObNQXBXpaVsmzYFukgzUloKu9Xwv37bNkhJafx6JLZqnbZoZt3MbF6Fjx/M7Goz28fM/mVmiyKf926MgkWk7tyDnnfVMN+0KbhPYZ4cag10d//C3TPcPQPoD2wEXgKuB3LdvSuQG7ktIglmjz2qL2W7bl0Q5K1ahVOTxEddLyzKARa7+zLgFODpSPvTwKmxLEySV35hPne+dyf5hflhl5LUevUKeuUbNpS3ffNNEOR77RVeXRI/dR1DPxt4PvL1fu6+EsDdV5qZFsmUWuUX5pMzMYeS0hJSU1LJHZFLVqessMtKKr/8Jbz2WuW2L7+sPr9ckk/UPXQzSwVOBqbW5QnMbJSZFZhZQXFxcV3rkySTtzSPktISSr2UktIS8pbmhV1S0vjtb4MeecUw//jjoEeuMG8e6jLkMgyY4+6rIrdXmVkHgMjn72p6kLuPd/dMd89s3759w6qVJi87PZvUlFRSLIXUlFSy07PDLqnJu/XWIMjHjStve/PNIMirXr4vya0uQy7nUD7cAjAduAD4a+TzKzGsS5JUVqcsckfkkrc0j+z0bA23NMD48dUXyHr++eqrIkrzYV5x87+dHWSWBhQCXdx9faStLTAFOAhYDpzp7t/v6vtkZmZ6QUFBg4sWac5eegmGD6/c9tBDMHp0OPVI/JnZbHev9e+tqHro7r4RaFulbQ3BrBcRibH8wvxqf8W8+271TSRuuKHyZhPSvOlKUZEEU3Um0N8G5DNiaOV93UaMgKef3sk3kGZLgS6SYHbMBPq+E5se+poRFe4bMqTyhswiFSnQRRJMt5ShlP75hkptnTvDkiUhFSRNhgJdJEGsWQPt2gH0q9QexbwFEUCBLhK6TZsgLa16u5aylbpSoIuEZGdL2W7ZAqmpjV+PNH11XZxLRBpoZ0vZrl8f3Kcwl/pSoIs0IrPqS9muWBEE+U9/Gk5NkjwU6CKNwKz6ePinnwZBfsAB4dQkyUeBLhJHNQX5e+8FQd69ezg1SfJSoIvEQbdu1YN82rQgyI8+OpyaJPkp0EVi6KSTgiD/8svytocfDoL89NPDq0uaBwW6SAxcdVUQ5DNmlLf9/vdBkF95ZXh1SfOieegiDXD//UFwVzR8OLzwQjj1SPOmQBeph3/8A845p3Jbz57wf/8XTj0ioEAXqZO8vGDFw4pSUmDbtlDKEalEgS4ShYULoVev6u1aOEsSiQJdZBeKiqBTp+rtCnJJRAp0kRqsWwd77129XUEuiUyBLlLBli3QqlX19tLS6muwiCQaBboIwdrjKSnV2zdtqjngRRJRVH0OM2tjZtPM7HMz+8zMsszsZjNbYWbzIh8nxLtYkXgwqx7m338fDK8ozKUpifaPyIeAN9z9cKAP8Fmk/QF3z4h8vB6XCkXipKaFs5YtC4K8pvFzkURXa6Cb2U+BwcAEAHcvcfd18S5MJF5qCvL584MgP+igcGoSiYVoeuhdgGLgSTOba2aPm9nukfuuMLMFZvaEmalPIwmtdevqQZ6bGwR5797h1CQSS9EE+m4E25A/5u59gQ3A9cBjwCFABrASuK+mB5vZKDMrMLOC4uLi2FQtUgd9+wZBvnlzedukSUGQH3dceHWJxFo0gV4EFLn7rMjtaUA/d1/l7qXuXgb8HRhY04Pdfby7Z7p7Zvv27WNTtUgUzjorCPJ588rb7rorCPJf/zq8ukTipdZAd/dvgUIz6xZpygE+NbMOFQ47DVgYh/pE6uy664Ignzq1vO3yy4Mgv/ba8OoSibdo56FfCTxrZqnAEuBC4GEzywAcWApcGpcKRaL0yCNwxRWV24YNg9c1/0qaiagC3d3nAZlVms+PfTkidffyy3DaaZXbunSBxYvDqUckLLpSVJqsDz+EQYOqt2u9FWmuFOjS5HzxBRx+ePV2Bbk0dwp0aTK+/RY6dKjeriAXCSjQJeFt2AB77FG9vays+oVCIs2ZAl0S1rZt0LJlze01rYwo0txphWdJOO5Bz7tqmG/aFNynMBepmQJdEopZ9Y0ktJStSHQU6JIQaloBsbBQS9mK1IUCXUK1//7Vg3zhwiDIO3YMpyaRpkqBLqE4+uggyFetKm97770gyHv0CK8ukaZMgS6N6okngiD/4IPythdfDIL86KPDq0skGSjQpVG8/HIQ5BdfXN726KNBkFddh0VE6kfz0CWu8vJgyJDKbS+/DKecEko5IklNPXSJizlzgh55xTCfMCHokSvMReJDgS4xtWhREOT9+5e33X13EOQXXRReXSLNgYZcJCa++QYOPLBy25gxQZiLSONQoEuDfP89tG1bue3882HixHDqEWnOFOhSLzWtgPjzn8O//hVOPSKiQJc62roVUlMrtx1xRHB1p5ayFQmXAl2iUlYG++4La9aUt7VpA6tXa/VDkUShWS6yS+7Qp08Q2hXDfMsWWLtWYS6SSBToslMnnBAsZbtgQXnbf/4ThHzVYRcRCV9UgW5mbcxsmpl9bmafmVmWme1jZv8ys0WRz1rkNElcfHEwHv7Pf5a3rV4dBHlNW8GJSGKItof+EPCGux8O9AE+A64Hct29K5AbuS1N2B/+EAT5E0+Ut21fk7zq1MSmJr8wnzvfu5P8wvywSxGJm1rfFDWznwKDgZEA7l4ClJjZKUB25LCngTzgungUKfH1wANwzTWV2z77DA4/PJx6Yi2/MJ+ciTmUlJaQmpJK7ohcsjplhV2WSMxF00PvAhQDT5rZXDN73Mx2B/Zz95UAkc/71vRgMxtlZgVmVlBcXByzwqXhli4NeuQVw/zjj4MeebKEOUDe0jxKSkso9VJKSkvIW5oXdkkicRFNoO8G9AMec/e+wAbqMLzi7uPdPdPdM9u3b1/PMiWWvvkG/vu/4bDDytv+9a8gyDMzw6srXrLTs0lNSSXFUkhNSSU7PTvskkTiIpp56EVAkbvPityeRhDoq8ysg7uvNLMOwHfxKlJiY/VquOsu+J//gW3bgjc/b7wROnUKu7L4yuqURe6IXPKW5pGdnq3hFklatQa6u39rZoVm1s3dvwBygE8jHxcAf418fiWulUq9rVsH998fjJVv3AjnnQc33QSHHBJ2ZY0nq1OWglySXrRXil4JPGtmqcAS4EKC4ZopZnYxsBw4Mz4lSn39+COMHQv33BNcBHTmmXDLLdC9e9iViUg8RBXo7j4PqGl0NSe25UgsbN4M48bBnXfCd9/BSSfBrbdC375hVyYi8aQrRZNISQn87W9w6KHwu99Br17w4Yfw6qsKc5HmQIGeBEpLg/XHDz8cLrsMDjoI3n4b3noLsjRsLNJsKNCbsLIymDIFevaECy4IVj+cMQM++KD6xswikvwU6E2QO7z2GvTrB7/6VbCA1rRpMHt2sKCW1iUXaZ4U6E2IO+Tmws9+Br/8ZTCL5ZlngtUQTz9dQS7S3CnQm4gPPoDjjgu2eSsqgvHjg/VWzjuvYWuSa9EqkeShHYsS3OzZ8Kc/BUvZ7rcfPPQQjBoFrVo1/Htr0SqR5KIeeoL65JNgGCUzEz76CP76V1i8GEaPjk2YgxatEkk26qEnmK++gptvhueeCzaTuPlmuPpq2Guv2D/X9kWrtvfQtWiVSNOmQE8Qy5fDbbfBk08G27uNGQPXXhvfjSW0aJVIclGgh+zbb+GOO4IrPAEuvzzYOWj//Rvn+bVolUjyUKCHZM0auPvuYPGskhK46CL44x+DqzxFROpDgd7I1q8PlrG9//5gHvm55wbj5IceGnZlItLUKdAbyYYNwcYSd98N338fzGC55Rbo0SPsykQkWSjQ42zz5uAioDvugFWrgkvzb70V+vcPuzIRSTYK9DjZuhWeeiqYuVJYCNnZ8MILMGhQ2JWJSLLShUUxVloKkyYFuwKNGgUHHBAsY/v22wpzEYkvBXqMlJUFPfDeveH884OLgl59FfLzISdHC2fFitaeiR+d26ZPQy4N5B6ss/LHP8LcucEmE1OmBG96ttCvy5jS2jPxo3ObHBQ5DTBzJhx9NJx4IqxbB08/DQsXBpsxK8xjT2vPxI/ObXJQ7NTDRx8FwyjHHQfLlgUbMn/xBYwY0bClbGXXtq89k2IpWnsmxnRuk4O5e+0HmS0F/gOUAtvcPdPMbgYuAYojh/3B3V/f1ffJzMz0goKCBhUcprlzg6VsZ8yA9u2DS/Qvuyx2qx9K7fIL87X2TJzo3CYuM5vt7pm1HleHQM9099UV2m4GfnT3e6MtqqkG+mefwU03Bdu8tWkTLJp15ZXBG58iIvEWbaDrTdFdWLIkuCz/2WchLS3onV9zTRDqIiKJJtoxdAfeNLPZZjaqQvsVZrbAzJ4ws73jUF8oiorg0kuhWzeYOjUI8a+/Dq7wVJiLSKKKNtAHuXs/YBjw32Y2GHgMOATIAFYC99X0QDMbZWYFZlZQXFxc0yEJY9WqYDOJQw8N1iW/9NKgl37PPdCuXdjViYjsWlSB7u7fRD5/B7wEDHT3Ve5e6u5lwN+BgTt57Hh3z3T3zPbt28eq7pj6/nu44Qbo0iVYQOvXv4ZFi4KvO3QIuzoRkejUGuhmtruZ7bn9a+B4YKGZVYy604CF8Skxfn74IRhG6dwZ7roLTjkFPv0UJkyAgw8OuzoRkbqJ5k3R/YCXLLh2fTfgOXd/w8yeMbMMgvH1pcClcasyxjZuhEceCUJ8zRo49dQg2Hv1CrsyEZH6qzXQ3X0J0KeG9vPjUlEcbdkCf/873H57sPXb0KHwl79AZq2TgUREEl+zmLa4bVtwWf6ttwabMQ8eHKy3cswxYVcmIhI7SX3pf1kZPPdcsJTtb34D++0Hb74JeXkKcxFJPkkZ6O7w0kvQp08wYyUtDV55BWbNgv/6Ly1lKyLJKakC3R3eeAMGDIDhw6GkBP7xj2ANlpNPVpCLSHJLmkB/551gbHzYsGDmypNPwiefwK9+paVsRaR5aBJRt6udVGbNguOPD/bsXLIEHn00WMp25EjYrVm85SsiEkj4yNvZTirz5weLZb36anBZ/n33wW9/C61bh12xiEg4Ej7Qq+6kMuWdBTz4ahZTpsBeewXzyEePhj33DLtSEZFwJXygb99JZcvqA+CdP/Pw/PNo3RpuvBF+/3vYO2nWeBQRaZiED/SsTlmct/ZzJow9kJTdjCuuNq6/PtgxSEREyiV8oAMMzjiI3UYFvfIDDwy7GhGRxNQkAv2884IPERHZuSYxbVFERGqnQBcRSRIKdBGRJKFAFxFJEgp0EZEkoUAXEUkSCnQRkSRX6Z8jAAADqElEQVShQBcRSRLm7o33ZGbFwLJ6PrwdsDqG5SQjnaNd0/mpnc7RroV1fg5291oXPGnUQG8IMytw98yw60hkOke7pvNTO52jXUv086MhFxGRJKFAFxFJEk0p0MeHXUAToHO0azo/tdM52rWEPj9NZgxdRER2rSn10EVEZBcSLtDNrI2ZTTOzz83sMzPLqnK/mdnDZvaVmS0ws35h1RqGKM5PtpmtN7N5kY+bwqo1DGbWrcLPPs/MfjCzq6sc02xfQ1Gen2b9GgIws9+Z2SdmttDMnjezVlXu/4mZTY68hmaZWXo4lVaWiBtcPAS84e5nmFkqkFbl/mFA18jHkcBjkc/NRW3nB+A9dz+pketKCO7+BZABYGYpwArgpSqHNdvXUJTnB5rxa8jMDgRGA0e4+yYzmwKcDTxV4bCLgbXufqiZnQ3cBfyq0YutIqF66Gb2U2AwMAHA3UvcfV2Vw04BJnrgI6CNmXVo5FJDEeX5kXI5wGJ3r3oxW7N9DVWxs/MjQWe3tZntRtBp+qbK/acAT0e+ngbkmJk1Yn01SqhAB7oAxcCTZjbXzB43s92rHHMgUFjhdlGkrTmI5vwAZJnZfDP7p5n1aOQaE8nZwPM1tDfn11BFOzs/0IxfQ+6+ArgXWA6sBNa7+5tVDtvxGnL3bcB6oG1j1lmTRAv03YB+wGPu3hfYAFxf5Ziafgs2l6k60ZyfOQSXCfcBxgIvN26JiSEyHHUyMLWmu2toay6vIaDW89OsX0NmtjdBD7wzcACwu5lV3dU4IV9DiRboRUCRu8+K3J5GEGBVj+lU4XZHqv85lKxqPT/u/oO7/xj5+nWgpZm1a9wyE8IwYI67r6rhvub8Gtpup+dHryF+Dnzt7sXuvhV4EfhZlWN2vIYiwzJ7Ad83apU1SKhAd/dvgUIz6xZpygE+rXLYdGBEZKbCUQR/Dq1szDrDEs35MbP9t4/lmdlAgn/jNY1aaGI4h50PJzTb11AFOz0/eg2xHDjKzNIi5yEH+KzKMdOBCyJfnwG87QlwUU/CXVhkZhnA40AqsAS4kMi7x+4+LnKC/wf4BbARuNDdC0Iqt9FFcX6uAH4LbAM2Ade4+4chlRsKM0sjGN/s4u7rI22XgV5DENX50WvI7BaC/1fbgLnAb4AbgQJ3nx6ZxvgM0JegZ362uy8Jq97tEi7QRUSkfhJqyEVEROpPgS4ikiQU6CIiSUKBLiKSJBToIiJJQoEuIpIkFOgiIklCgS4ikiT+Pxo+oAycVSTHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#学习时长\n",
    "X_train=np.array([6,7,7.5,8,6.6,8.1,6.8,6.9,7.3,6.9])\n",
    "X_train=X_train.reshape(-1,1)\n",
    "#学习成绩\n",
    "y_train=np.array([53,60,56,79,58,85,70,56,69,76])\n",
    "#L2损失\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr=LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "y_pre=lr.predict([[7.2]])\n",
    "print(\"学习时长为7.2的预测分数为：\",y_pre)\n",
    "plt.plot(X_train,y_train,'g.',label='train samples')\n",
    "plt.plot(X_train,lr.predict(X_train),'b-',label='L2 loss')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "学习时长为7.2的预测分数为： [67.79339269]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#HUBER损失线性回归\n",
    "from sklearn.linear_model import HuberRegressor\n",
    "hr=HuberRegressor()\n",
    "hr.fit(X_train,y_train)\n",
    "print(\"学习时长为7.2的预测分数为：\",hr.predict([[7.2]]))\n",
    "plt.plot(X_train,hr.predict(X_train),'b-',label='HuberLoss')\n",
    "plt.plot(X_train,y_train,'g.',label='traim samples')\n",
    "plt.legend(loc=\"upper left\")\n",
    "p=hr.predict(X_train)\n",
    "for idx, m in enumerate(X_train):\n",
    "    plt.plot([m, m],[y_train[idx],p[idx]], 'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：根据汽车使用年限，预测二手车价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用年限数据集：\n",
      "[ 4  4  5  5  7  7  8  9 10 11]\n",
      "汽车价格数据集:\n",
      "[6300 5800 5700 4500 4500 4200 4100 3100 2100 2500]\n",
      "============================================================\n",
      "预测12年二手车的价格是： [1592.5]\n",
      "模型的回归系数w是： [-537.5] 模型的截距b是: 8042.5\n",
      "the function is: y=-537.500000x+8042.500000\n",
      "R2 score is: 0.9060679883512545\n",
      "metrics MSE score is: 167725.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "结论：汽车使用年限与价格是负相关\n"
     ]
    }
   ],
   "source": [
    "#求L2损失回归模型，对新测试数据12年预测其价格\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "X_train=np.array([4,4,5,5,7,7,8,9,10,11])\n",
    "print(\"使用年限数据集：\")\n",
    "print(X_train)\n",
    "X_train=X_train.reshape(-1,1)\n",
    "y_train=np.array([6300,5800,5700,4500,4500,4200,4100,3100,2100,2500])\n",
    "print(\"汽车价格数据集:\")\n",
    "print(y_train)\n",
    "print(\"=\"*60)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr=LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "print(\"预测12年二手车的价格是：\",lr.predict([[12]]))\n",
    "print(\"模型的回归系数w是：\",lr.coef_,\"模型的截距b是:\",lr.intercept_)\n",
    "print(\"the function is: \"\"y=%fx+%f\"%(lr.coef_,lr.intercept_))\n",
    "print(\"R2 score is:\",lr.score(X_train,y_train))\n",
    "ms=sklearn.metrics.mean_squared_error(y_train,lr.predict(X_train))\n",
    "print(\"metrics MSE score is:\",ms)\n",
    "plt.plot(X_train,y_train,'g.',label='train samples')\n",
    "plt.plot(X_train,lr.predict(X_train),'b-',label='L2 Loss')\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.title(\"the regression of used car and it's price\")\n",
    "plt.xlabel(\"year of used car\")\n",
    "plt.ylabel(\"price of used car (dollars)\")\n",
    "plt.grid()\n",
    "p=lr.predict(X_train)\n",
    "for idx, m in enumerate(X_train):\n",
    "    plt.plot([m, m],[y_train[idx],p[idx]], 'r-')\n",
    "plt.show()\n",
    "print(\"结论：汽车使用年限与价格是负相关\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 校验集数据划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#核心方法：train_test_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1]\n",
      " [2 3]\n",
      " [4 5]\n",
      " [6 7]\n",
      " [8 9]]\n",
      "[0, 1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "X,y=np.arange(10).reshape((5,2)),range(5)\n",
    "print(X)\n",
    "print(list(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 5]\n",
      " [0 1]\n",
      " [6 7]]\n",
      "******************************\n",
      "[[2 3]\n",
      " [8 9]]\n",
      "******************************\n",
      "[2, 0, 3]\n",
      "******************************\n",
      "[1, 4]\n",
      "******************************\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.33,random_state=42)\n",
    "print(X_train)\n",
    "print(\"*\"*30)\n",
    "print(X_test)\n",
    "print(\"*\"*30)\n",
    "print(y_train)\n",
    "print(\"*\"*30)\n",
    "print(y_test)\n",
    "print(\"*\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K折交叉验证数据集划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "StratifiedKFold(n_splits=2, random_state=None, shuffle=False)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "X=np.array([[1,2],[3,4],[1,2],[3,4]])\n",
    "y=np.array([0,0,1,1])\n",
    "skf=StratifiedKFold(n_splits=2)\n",
    "k=skf.get_n_splits(X,y)#获取折数K的方法\n",
    "print(k)\n",
    "print(skf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: [1 3] TEST: [0 2]\n",
      "TRAIN: [0 2] TEST: [1 3]\n"
     ]
    }
   ],
   "source": [
    "for train_index,test_index in skf.split(X,y):\n",
    "    print(\"TRAIN:\",train_index,\"TEST:\",test_index)\n",
    "    X_train,X_test=X[train_index],X[test_index]\n",
    "    y_train,y_test=y[train_index],y[test_index]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K折交叉验证评估模型性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#主要两个方法\n",
    "#cross_val_score()\n",
    "#cross_validate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.33150734 0.08022311 0.03531764]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets,linear_model\n",
    "from sklearn.model_selection import cross_val_score\n",
    "diabets=datasets.load_diabetes()\n",
    "X=diabets.data[:150]\n",
    "y=diabets.target[:150]\n",
    "lasso=linear_model.Lasso()\n",
    "print(cross_val_score(lasso,X,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets,linear_model\n",
    "from sklearn.model_selection import cross_validate \n",
    "from sklearn.metrics.scorer import make_scorer\n",
    "from sklearn.svm import LinearSVC\n",
    "diabetes=datasets.load_diabetes()\n",
    "X=diabetes.data[:150]\n",
    "y=diabetes.target[:150]\n",
    "lasso=linear_model.Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fit_time', 'score_time', 'test_score']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_result=cross_validate(lasso,X,y,return_train_score=False,cv=4)\n",
    "sorted(cv_result.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.3392459 ,  0.12286347,  0.16482017, -0.04610521])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_result[\"test_score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3635.51152303 -3573.34242148 -6114.78229547]\n",
      "[0.28010158 0.39088426 0.22784852]\n"
     ]
    }
   ],
   "source": [
    "scores=cross_validate(lasso,X,y,scoring=('r2','neg_mean_squared_error'),return_train_score=True)\n",
    "print(scores['test_neg_mean_squared_error'])\n",
    "print(scores['train_r2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型的超参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#主要API：class sklearn.model_selection.GridSearchCV()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'kernel': ('linear', 'rbf'), 'C': [1, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm,datasets\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "iris=datasets.load_iris()\n",
    "parameters={'kernel':('linear','rbf'),'C':[1,10]}\n",
    "svc=svm.SVC()\n",
    "clf=GridSearchCV(svc,parameters)\n",
    "clf.fit(iris.data,iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mean_fit_time',\n",
       " 'mean_score_time',\n",
       " 'mean_test_score',\n",
       " 'mean_train_score',\n",
       " 'param_C',\n",
       " 'param_kernel',\n",
       " 'params',\n",
       " 'rank_test_score',\n",
       " 'split0_test_score',\n",
       " 'split0_train_score',\n",
       " 'split1_test_score',\n",
       " 'split1_train_score',\n",
       " 'split2_test_score',\n",
       " 'split2_train_score',\n",
       " 'std_fit_time',\n",
       " 'std_score_time',\n",
       " 'std_test_score',\n",
       " 'std_train_score']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(clf.cv_results_.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before transform: [[1, 5, 1, 2, 10], [2, 6, 3, 2, 7], [3, 7, 5, 6, 4], [4, 8, 7, 8, 1]]\n",
      "scale_ is : [1.11803399 1.11803399 2.23606798 2.59807621 3.35410197]\n",
      "mean_ is : [2.5 6.5 4.  4.5 5.5]\n",
      "var_ is : [ 1.25  1.25  5.    6.75 11.25]\n",
      "after transform: [[-1.34164079 -1.34164079 -1.34164079 -0.96225045  1.34164079]\n",
      " [-0.4472136  -0.4472136  -0.4472136  -0.96225045  0.4472136 ]\n",
      " [ 0.4472136   0.4472136   0.4472136   0.57735027 -0.4472136 ]\n",
      " [ 1.34164079  1.34164079  1.34164079  1.34715063 -1.34164079]]\n",
      "去均值和方差归一化。且是针对每一个特征维度来做的，而不是针对样本。\n"
     ]
    }
   ],
   "source": [
    "def test_StandardScaler():\n",
    "    X=[[1,5,1,2,10],\n",
    "      [2,6,3,2,7],\n",
    "      [3,7,5,6,4,],\n",
    "      [4,8,7,8,1]]\n",
    "    print(\"before transform:\",X)\n",
    "    scaler=StandardScaler()\n",
    "    scaler.fit(X)\n",
    "    print(\"scale_ is :\",scaler.scale_)#缩放比例，同时也是标准差\n",
    "    print(\"mean_ is :\",scaler.mean_)#均值\n",
    "    print(\"var_ is :\",scaler.var_)#方差\n",
    "    print(\"after transform:\",scaler.transform(X))\n",
    "test_StandardScaler() \n",
    "print(\"去均值和方差归一化。且是针对每一个特征维度来做的，而不是针对样本。\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 波士顿房价预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "#导入必要工具包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM  ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296       15   \n",
       "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242       17   \n",
       "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242       17   \n",
       "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222       18   \n",
       "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222       18   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据\n",
    "df=pd.read_csv(r\"E:\\boston_housing.csv\")\n",
    "#显示数据的前5行\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null int64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null int64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null int64\n",
      "TAX        506 non-null int64\n",
      "PTRATIO    506 non-null int64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "MEDV       506 non-null float64\n",
      "dtypes: float64(9), int64(5)\n",
      "memory usage: 55.4 KB\n"
     ]
    }
   ],
   "source": [
    "#数据相关信息\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 14)\n"
     ]
    }
   ],
   "source": [
    "#数据的形状\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
      "       'PTRATIO', 'B', 'LSTAT', 'MEDV'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#数据的列名称\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.347826</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.083004</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.310593</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.280574</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677082</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.347826   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.310593    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677082   12.000000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.083004  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.280574   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.000000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.000000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.000000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.000000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对于数值型特征，可以用describe方法探索基本统计学特征\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单变量分布分析\n",
    "#### 对于单个变量，可以用直方图，箱体图等方式查看其大致分布\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#seaborn的distplot方法可以对数值型特征绘制直方图\n",
    "fig=plt.figure()\n",
    "sns.distplot(df[\"MEDV\"],bins=30,kde=True)\n",
    "plt.xlabel(\"Media value of owner-occupied homes\",fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用DataFrame的hist()方法也可绘制直方图\n",
    "fetures=df.columns\n",
    "df[fetures].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23b66f48860>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#箱体图boxplot和提琴图violinplot\n",
    "_,axes=plt.subplots(1,2,sharey=True,figsize=(6,4))\n",
    "sns.boxplot(data=df[\"MEDV\"],ax=axes[0]);\n",
    "sns.violinplot(data=df[\"MEDV\"],ax=axes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类别型特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "#可以用频率表value_counts给出每个特征取值的样本数目，或者采用countplot方法绘制条形直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    471\n",
       "1     35\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"CHAS\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df[\"CHAS\"],order=[0,1]);\n",
    "plt.xlabel(\"Charles River\");\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sns.countplot(df[\"RAD\"]);\n",
    "plt.xlabel(\"index of accessibility to radial highway\");\n",
    "plt.ylabel(\"你还\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数值特征-数值特征corr()方法，heatmap()方法,散点图scatter()\n",
    "#数值特征-类别特征lmplot()函数的Hue参数\n",
    "#类别特征-类别特征 设置Hue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 相关矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23b6a9970f0>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=df.columns\n",
    "data_corr=df.corr()\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#得到相关系数的绝对值，通常认为相关系数的绝对值大于0.5的特征为强相关\n",
    "data_corr=data_corr.abs()\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "sns.heatmap(data_corr,mask=data_corr<0.5,cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x23b6afd4eb8>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#散点图\n",
    "plt.scatter(df[\"RM\"],df['MEDV'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"RM\",y=\"MEDV\",data=df,kind=\"scatter\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('RM','MEDV',data=df,hue='CHAS',fit_reg=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23b6adc0390>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes=plt.subplots(1,2,sharey=True,figsize=(10,4))\n",
    "sns.boxplot(x='CHAS',y='MEDV',data=df,ax=axes[0])\n",
    "sns.violinplot(x='CHAS',y=\"MEDV\",data=df,ax=axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 799.125x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#鸢尾花多变量图例子\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set(style=\"ticks\", color_codes=True)\n",
    "iris = sns.load_dataset(\"iris\")\n",
    "g = sns.pairplot(iris,hue=\"species\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 14)\n"
     ]
    }
   ],
   "source": [
    "#数据去噪\n",
    "#删除y>50的样本\n",
    "#df=df[df.MEDV<50]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从原始数据中分离x和y\n",
    "y=df['MEDV']\n",
    "X=df.drop('MEDV',axis=1)\n",
    "#对y值（房价）做log变换\n",
    "log_y=np.log1p(y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "#离散型特征编码\n",
    "#独热编码\n",
    "#可以用pandas的get_dummies方法或者sklearn中的onehotencoder()来实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RAD_1</th>\n",
       "      <th>RAD_2</th>\n",
       "      <th>RAD_3</th>\n",
       "      <th>RAD_4</th>\n",
       "      <th>RAD_5</th>\n",
       "      <th>RAD_6</th>\n",
       "      <th>RAD_7</th>\n",
       "      <th>RAD_8</th>\n",
       "      <th>RAD_24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RAD_1  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  RAD_8  RAD_24\n",
       "0      1      0      0      0      0      0      0      0       0\n",
       "1      0      1      0      0      0      0      0      0       0\n",
       "2      0      1      0      0      0      0      0      0       0\n",
       "3      0      0      1      0      0      0      0      0       0\n",
       "4      0      0      1      0      0      0      0      0       0"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['RAD'].astype('object')\n",
    "x_cat=X['RAD']\n",
    "x_cat=pd.get_dummies(x_cat,prefix=\"RAD\")\n",
    "X=X.drop('RAD',axis=1)\n",
    "feat_names=X.columns\n",
    "x_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数值型特征标准化\n",
    "#避免原始数据差异过大，导致训练得到的参数权重单位不一致，无法比较各特征的重要性\n",
    "#一些算法（如随机梯度下降法）要求在各特征尺度差不多的情况下才能保证收敛\n",
    "#两种方法 ：StandardScaler均值方差标准化(均值为0，方差为1) minmax_scale最大最小值标准化（0~1）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nss_x=StandardScaler()\\nss_y=StandardScaler()\\nss_log_y=StandardScaler()\\nX=ss_x.fit_transform(X)\\ny=ss_y.fit_transform(df[\"MEDV\"].values.reshape(-1,1))\\nlog_y=ss_log_y.fit_transform(np.log1p(df[\"MEDV\"]).values.reshape(-1,1))\\n'"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# StandardScaler标准化方式\n",
    "'''\n",
    "ss_x=StandardScaler()\n",
    "ss_y=StandardScaler()\n",
    "ss_log_y=StandardScaler()\n",
    "X=ss_x.fit_transform(X)\n",
    "y=ss_y.fit_transform(df[\"MEDV\"].values.reshape(-1,1))\n",
    "log_y=ss_log_y.fit_transform(np.log1p(df[\"MEDV\"]).values.reshape(-1,1))\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#minmax_scale标准化方式\n",
    "mms_x=MinMaxScaler()\n",
    "mms_y=MinMaxScaler()\n",
    "mms_log_y=MinMaxScaler()\n",
    "X=mms_x.fit_transform(X)\n",
    "y=mms_y.fit_transform(df[\"MEDV\"].values.reshape(-1,1))\n",
    "log_y=mms_log_y.fit_transform(np.log1p(df[\"MEDV\"]).values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存特征工程的结果到CSV文件\n",
    "fe_data=pd.DataFrame(data=X,columns=feat_names,index=df.index)\n",
    "fe_data=pd.concat([fe_data,x_cat],axis=1,ignore_index=False)\n",
    "#加上标签\n",
    "fe_data[\"MEDV\"]=y\n",
    "fe_data[\"log_MEDV\"]=log_y\n",
    "#保存结果到文件\n",
    "fe_data.to_csv(\"C:\\\\Users\\\\zhouhao\\\\Desktop\\\\csdnpptb\\\\SS_FE_BOSTON_HOUSING.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
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       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
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       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        TAX  PTRATIO    ...     RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  \\\n",
       "0  0.208015      0.3    ...         0      0      0      0      0      0   \n",
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       "4  0.066794      0.6    ...         0      1      0      0      0      0   \n",
       "\n",
       "   RAD_8  RAD_24      MEDV  log_MEDV  \n",
       "0      0       0  0.422222  0.666856  \n",
       "1      0       0  0.368889  0.619696  \n",
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       "4      0       0  0.693333  0.852567  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 23 columns):\n",
      "CRIM        506 non-null float64\n",
      "ZN          506 non-null float64\n",
      "INDUS       506 non-null float64\n",
      "CHAS        506 non-null float64\n",
      "NOX         506 non-null float64\n",
      "RM          506 non-null float64\n",
      "AGE         506 non-null float64\n",
      "DIS         506 non-null float64\n",
      "TAX         506 non-null float64\n",
      "PTRATIO     506 non-null float64\n",
      "B           506 non-null float64\n",
      "LSTAT       506 non-null float64\n",
      "RAD_1       506 non-null uint8\n",
      "RAD_2       506 non-null uint8\n",
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      "RAD_24      506 non-null uint8\n",
      "MEDV        506 non-null float64\n",
      "log_MEDV    506 non-null float64\n",
      "dtypes: float64(14), uint8(9)\n",
      "memory usage: 59.9 KB\n"
     ]
    }
   ],
   "source": [
    "fe_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
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       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        TAX  PTRATIO    ...     RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  \\\n",
       "0  0.208015      0.3    ...         0      0      0      0      0      0   \n",
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       "4  0.066794      0.6    ...         0      1      0      0      0      0   \n",
       "\n",
       "   RAD_8  RAD_24      MEDV  log_MEDV  \n",
       "0      0       0  0.422222  0.666856  \n",
       "1      0       0  0.368889  0.619696  \n",
       "2      0       0  0.660000  0.833335  \n",
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       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#波士顿房价：用岭回归模型训练数据\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt  \n",
    "from sklearn.linear_model import RidgeCV \n",
    "from sklearn.model_selection import train_test_split\n",
    "mydata=pd.read_csv(\"C:\\\\Users\\\\zhouhao\\\\Desktop\\\\csdnpptb\\\\SS_FE_BOSTON_HOUSING.csv\")\n",
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数矩阵:\n",
      " [-0.09840996  0.07534392 -0.0255328   0.09250746 -0.09317376  0.34884089\n",
      "  0.01942861 -0.27115399 -0.04583467 -0.21091335  0.10592203 -0.46260203\n",
      " -0.08651718 -0.01368628  0.02923675 -0.02107846 -0.01246682 -0.04665044\n",
      "  0.06261117  0.03833648  0.05021477]\n",
      "线性回归模型:\n",
      " RidgeCV(alphas=[0.1, 1.0, 10.0], cv=None, fit_intercept=True, gcv_mode=None,\n",
      "    normalize=False, scoring=None, store_cv_values=False)\n",
      "交叉验证最佳alpha值 1.0\n",
      "训练得分是： 0.7377193969486561\n"
     ]
    }
   ],
   "source": [
    "my_X=mydata.drop(\"MEDV\",axis=1)\n",
    "my_X=my_X.drop(\"log_MEDV\",axis=1)\n",
    "my_y=mydata[\"MEDV\"]\n",
    "X_mytrain,X_mytest,y_mytrain,y_mytest=train_test_split(my_X,my_y,test_size=0.33,random_state=2)\n",
    "rcv = RidgeCV(alphas=[0.1, 1.0, 10.0])\n",
    "rcv.fit(X_mytrain, y_mytrain)   # 线性回归建模\n",
    "print('系数矩阵:\\n',rcv.coef_)\n",
    "print('线性回归模型:\\n',rcv)\n",
    "print('交叉验证最佳alpha值',rcv.alpha_)  \n",
    "# 使用模型预测\n",
    "predicted = rcv.predict(X_mytest)\n",
    "score=rcv.score(X_mytrain,y_mytrain)\n",
    "print(\"训练得分是：\",score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.50772316]\n"
     ]
    }
   ],
   "source": [
    "#测试第一行数据\n",
    "one=np.array([0.000000,0.18,0.067815,0.0,0.314815,0.577505,0.641607,0.269203,0.208015,0.3,1.000000,0.089680,1,0,0,0,0,0,0,0,0])\n",
    "one=one.reshape(1,-1)\n",
    "predictsingle=rcv.predict(one)\n",
    "print(predictsingle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最小二乘法容易过拟合，如果特征之间存在共线性，会出现无解的情况      \n",
    "岭回归加了惩罚项，抑制了过拟合，使得模型更加简单，有效的减弱了过拟合的结果      \n",
    "Lasso回归的好处是容易得到稀疏解，使得一些回归系数w变为0，因此也减弱了过拟合的结果      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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